Speaker identification based on vector quantization with adaptive discriminative techniques

Abstract

In this work, speaker identification (SI) approach which is based on vector quantization (VQ) is presented. The method employs adaptive techniques to select the optimal parameters of the discriminative function. The proposed adaptive discriminative VQ based SI (ADVQSI) technique considers the interspeaker variation between each speaker and all speakers in the SI group in order to enlarge the speakers' template differences. For each speaker, the speech feature vector space is divided into subspaces. Different discriminative weights are given to different subspaces. Subspaces with larger discriminative weights play a more important role in the SI decision. The performance of ADVQSI is analyzed and tested experimentally. The experimental results confirm the performance improvement employing the proposed technique in comparison with the existing VQ technique for SI (VQSI) and recently reported discriminative VQ techniques for SI (DVQSI).